Enhancing insight-driven customer interactions

ABSTRACT

Insight-driven interactions with customers may be enhanced in a holistic approach. A customer relationship management (“CRM”) methodology may include: ( 1 ) evaluating a customer strategy; ( 2 ) identifying customer segments from a customer base; ( 3 ) forming an interaction strategy; ( 4 ) defining a series of experiences based on the strategy; ( 5 ) applying those interactions with customers during interactions; and ( 6 ) monitoring the results of the customer interactions. A computer aid may preferably guide a user through some of these steps. A modular, vendor-independent, centralized, rules-based engine may perform processing to deliver tailored customer experiences, relying on values for prioritized experiences identified through use of the computer aid.

RELATED APPLICATIONS

This application is related to commonly assigned co-pending patentapplications “Enhancing Insight-Driven Customer Interactions with aWorkbench” (Attorney Docket No. 60021-379701, Ser. No. ______) and“Enhancing Insight-Driven Customer Interactions With An OptimizingEngine” (Attorney Docket No. 60021-379801, Ser. No. ______), both filedMarch 2004, and both of which are incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates to customer relationship management (“CRM”). Moreparticularly, the invention relates to a system, method and computerprogram for enhancing interactions between a customer and a companythrough the use of: (1) a guided customer experience managementmethodology, (2) a software application toolset that allows businessusers to analyze the effectiveness of previous treatments and define newtreatments to apply during customer interactions, and (3) a rules-basedengine for applying those treatments in real-time as customers interactwith the business and capture performance data, regardless of thecustomer interaction channel.

BACKGROUND OF THE INVENTION

A. Implementing CRM Theories is not Readily Accomplished

A goal of CRM is to help a business maximize the value of its customerrelationships by enhancing cost-to-serve and revenue opportunities. Bybetter understanding customers' needs and the value they bring to thebusiness, a company can tailor the way it markets, sells, and servicescustomers so that customers who contribute to the profits of thebusiness will buy more, but more profitable products or services, buythem more often, and continue to do business with the company. To putthis model into practice, a company should be proficient in one or moreareas. For example, a company may need to be proficient in: (1) defininga customer strategy; (2) aggregating customer data; (3) drawing insightsinto customers' needs based upon analysis of the customer data; (4)defining appropriate customer treatments based upon customer insights;(5) applying treatments in real-time or batch, regardless of the channelused by the customer to interact with the business; (6) capturing theresults of the interactions and feeding it back into the insight processso more accurate assessments can be made in ensuing cycles.

Companies have had difficulty developing and implementing bothindividual proficiencies and end-to-end proficiencies required toachieve the goals of CRM. Some companies have developed customer datawarehouses containing historical customer transaction data, sometimesappended with household data. Some companies have developed analyticalprograms that have run against the data warehouse to determine effectivemarketing programs. And finally, some companies have been able to takethe results of customer insights to manually tailor interactions withcustomers through specific customer interaction channels.

Companies have struggled in the proficiency and process of defining acustomer experience and associated treatments. Presently, there is noprocess to holistically define the customer experience across allcontact points, products and services, and there are also no tools tocapture and automate treatments in a systematized approach. Mostorganizations today determine experiences on an ad hoc basis and in asilo fashion across marketing, sales, and service. This ultimatelycreates inconsistent experiences and treatments across channels as wellas increases maintenance of all the channel applications.

But while certain companies have had limited success at implementingsome of the proficiencies individually, companies are challenged toimplement all of the capabilities needed to completely realize goals ofCRM. Companies have struggled to implement insight driven interactions.This includes a systematic, fact-driven process for defining customertreatments tailored to individual customer segments, applying theintended treatments in real-time (or batch) across the variousinteraction channels, and feeding back the interaction results tore-train the analytical models. They have not been able to leverage theprocess of defining intended customer interactions such that it actuallyfeeds the data repository needed to drive the actual interactions. Theyhave not been able to streamline the process of building analyticalmodels and driving the results into interactions quickly enough tooptimize the results. They have not had a centralized means by whichthey could define and implement intended customer treatments across allcustomer interaction channels.

B. Maintaining Treatments is Time Consuming

Since delivering the appropriate customer experience is essential toCRM, companies may desire to control the various interactions between acustomer and the company in order to enhance or optimize the resultingexperience. There are presently systems that assist a company ininteracting with customers. For example, IVR systems allow a customer touse a telephone to find out a balance and payment due date as a type ofself-service interaction. Or, a customer service representative may relyon a CRM software system to retrieve and store information about acustomer when the customer calls about a problem. Unfortunately, thesesystems rely on their own rules processing and internalcode/configurations to make interaction decisions based on customerinsight. Since each business division and each contact channel hasdifferent requirements for an interaction system, each system istypically coded, modified and configured individually to meet specificbusiness requirements. Once a customized system has been created, itrequires continual maintenance and may be difficult and/or timeconsuming to recode, reconfigure or update. Whenever a modification tothe system is to be made, a new process of defining requirements,developing designs, and building the modifications must take place. Thisis very time consuming and inefficient, especially for any systems thatare tightly coupled with backend systems, and code that is not welldocumented or modularized, such as IVR systems. In addition, mostbusinesses support more than one contact channel. Trying to create aconsistent experience across more than one channel system (i.e. IVR,Web, Agent Desktop, E-Mail, Kiosk, etc.) requires code changes to occuron all channels which is again time consuming and inefficient. Forexample, an organization may want to present a certain type of offer tocustomers via an IVR system and a Internet website. Whenever acustomization is to be made to how a customer can receive a specifictreatment—such as receiving the same offer whether on the web or in theIVR—modifications must be made to the IVR system as well as to the webserver. Modifying a number of channels to incorporate one change isinefficient and often error prone.

In addition, working across functionality is also inefficient and errorprone. Because many organizations are aligned around channels (i.e.,IVR, Web, Agent) or functions (i.e., Marketing, Sales, Service) asopposed to customer or customer segments, the effort to define and buildconsensus, document, and act on consistent strategies is a challenge.Often, these barriers exist because of misaligned priorities (generatesales versus lower cost to serve versus maximize customer lifetimevalue), misaligned incentive programs (higher commissions for new salesversus retention cross sales activities), and/or focus on channel as“the in solution”—i.e., web. Aspects of the present invention facilitatebreaking down these barriers by taking a broad perspective of thecustomer lifecycle, the ways to drive value across the relationship byfocusing on marketing, sales, billing and service actions, as well aschannels.

Because coding is involved when making changes to the web, IVR, agentdesktop or any other channel, the change must be implemented by someonein the IT department of the business. Whenever, for example, a business'marketing department develops a new campaign and wishes to add a newcustomer treatment to a channel it may require two time-consuming stepsin the implementation cycle by specialized IT resources: (1) translatingthe treatment into technical specifications that will meet the businessoutcomes for the target customer segment (e.g., increased customersatisfaction, increased sales, etc.), and (2) scheduling and completingthe implementation of the request. Meanwhile the marketing department(or other team requesting the treatment change) must wait for theupdates and modifications to be implemented.

The need for constant customizations and modifications also creates anopportunity for inconsistencies. Either the modification may not be madeto all the interaction channels, or the new prompts and content may beadded differently to each contact channel. Or, current systems for achannel may not offer the same capability as another channel.Additionally, if a customer repeatedly makes the same inquiry, thecustomer must still proceed through an entire menu for obtaining thedesired response. This is also inefficient.

C. To Maximize Value, Customize the Choices Presented to the Customer

As previously mentioned, current customer interaction systems may becustomized with options/treatments that are presented to the users. Someof these options may be based on characteristics of the callingcustomer. For example, in an IVR system, a customer may be able to pressor say “1” to hear options in English and to press or say “2” to hearthe options in Spanish. The customer's response then determines how therest of the interaction proceeds (i.e., either in English or Spanish).While some of level of customization is available in existinginteraction systems, a systematic approach that offers customizationfrom a central location to all of the communication channels at once isnot available.

What is needed is a methodology that can be used systematically andholistically to guide a company to evaluate, implement, improve, andmaintain a CRM strategy that can gain and leverage insight aboutcustomers through their interactions with the company. What is alsoneeded is a computerized toolset to present and document such asystematic methodology and capture the intended customer treatments foruse in controlling the interaction with the customers. Furthermore, whatis needed is a computer system that can leverage the information definedby the methodology when interacting with customers to enhance theexperience across all interaction channels, where treatments can bequickly manipulated by business users.

What is also needed is a system that can derive insight frominteractions to further enhance future interactions with customers. Whatis also needed is a way to easily, quickly and consistently makemodifications to how the interaction with the customer will bedelivered. What is needed is a system that can be used by anon-technical employee who understands the business goals andspeed-to-market urgency rather than requiring generic programming froman IT professional. When a change is made to the IVR treatments, forexample, what is needed is a way to readily make the same change to thewebsite, agent desktop, IVR and all other channels simultaneously withlittle modifications to the systems.

SUMMARY OF THE INVENTION

The invention relates to consistently enhancing the customer interactionexperience across all the channels through which a customer interactswith a company. It includes the methods, systems, and computer programsneeded to enhance customer experiences by defining a set of prioritizedexperiences and associated treatments and correlating them with acustomer interaction strategy for the company. The prioritizedexperiences are stored and then dynamically applied during interactionswith customers across the variety of customer interaction channels.Customer interaction results are then captured so that they can beanalyzed and used to define and enhance future interaction experiences.

The invention is preferably a solution leveraging a method, a computersystem, and a computer programming that includes: (1) evaluating acustomer strategy; (2) identifying customer segments from a customerbase; (3) translating that customer strategy into an interactionstrategy; (4) defining and automating experiences based on the strategy;(5) delivering and executing enhanced treatments to customers; and (6)monitoring the results of the customer interactions to enhance (i.e.,optimize, tune or improve) future interactions. A computer aid maypreferably guide a user through some of these steps. That computerprogram may preferably allow a business-user to set values needed todefine the series of experiences and capture the preferred treatments ina database to direct the customer interaction. A modular, rules-based,engine preferably performs the processing required to deliver tailoredcustomer experiences during interactions with customers across theavailable customer interaction channels, leveraging the values setthrough the computer aid. In addition to full-service channels (where acustomer interacts with a company representative), the engine may alsosupport self-service interactions, such as a customer using a phonekeypad and an IVR system to retrieve account information. The rulesprocessed by the engine may be based on insights gained by assessingprior customer interactions and associated customer behavior and be usedto generate insight to control future customer interactions. The rulesmay also be based on what has been assessed about customers so that atargeted customer treatment can be applied to each customer based oninsight.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood with reference to the followingdescription, claims, and accompanying drawings where:

FIG. 1 is a flowchart of the major steps involved in one preferredembodiment of the invention.

FIG. 2-1 is a block diagram of a preferred system for enhancing customerinteractions according to one embodiment of the invention.

FIG. 2-2 is a block diagram illustrating a preferred technicalarchitecture for the system from FIG. 2-1.

FIG. 2-3 is a diagram of an example CIR structure.

FIG. 3-1 illustrates an opening screen of a workbench tool that is oneembodiment of the present invention.

FIG. 3-2 is a screenshot of the area in the workbench tool shows thetasks available for each of the phases.

FIG. 3-3 is a screenshot of the workbench area that lists the currentsegments by name and with a short description.

FIG. 3-4 is a screenshot of the workbench area that is a report showingdetails of one segment.

FIG. 3-5 is a screenshot of the workbench area displayed when a userdrills down through the report to discover the percentage of customersin each segment.

FIG. 3-6 is a screenshot of the workbench area displayed when the userdrills down even further to see the population for each segment.

FIG. 3-7 is a screenshot of the workbench that allows a user to evaluateand create sub-segments (segments within segments).

FIG. 3-8 is a screenshot of the workbench that allows the user toidentify (based on the existing lists of industry specific interactionreasons), add and capture all the current reasons customers call theorganization (interaction reasons).

FIG. 3-9 is a screenshot of the workbench area that allows a user to setup the current channel mix.

FIG. 3-10 is a screenshot of the workbench area that illustrates thatthe current channel mix can be shown by count as well as by percentage.

FIG. 3-11 is a screenshot of the workbench area that shows howinformation is documented regarding the experiences and capabilitiesthat occur on each channel for each interaction type.

FIG. 3-12 is a screenshot of the Enterprise Value Calculator that helpsevaluate key value drivers through cost and revenue metrics.

FIG. 3-13 is a screenshot of the inputting data in the Enterprise ValueCalculator.

FIG. 3-14 is a screenshot of an output report from the Enterprise ValueCalculator.

FIG. 3-15 is a screenshot of the interaction reasons ranked and theability to use or execute on one of these interaction reasons.

FIG. 3-16 is a screenshot of the workbench area that displays the future(‘to be”) channel mix entered by interaction reason and segment.

FIG. 3-17 is a screenshot of the workbench area that depicts theindustry specific treatment taxonomy and shows the ability for a user toadd, modify, or delete treatments.

FIG. 3-18 is a screenshot of the workbench that illustrates the abilityto create codes and values for each treatment.

FIG. 3-19 is a screenshot of the workbench that allows a user to rank orprioritize treatments.

FIG. 3-20 is a screenshot of the workbench area that is used to capturenew future experiences for each segment, interaction reason, andchannel.

FIGS. 3-21 and 3-22 are screenshots of the workbench area that allowsthe user to define and setup the treatment automation by assigningvalues to interaction reasons, and segments.

FIG. 3-23 is a screenshot of the Experience Monitor and its dashboard.

FIG. 4 is a sample listing of the treatment taxonomy leveraged withinthe workbench.

FIG. 5 is an illustration of the impact on consolidating codemodification by moving the treatment logic to a single location.

FIG. 6 provides an overview of the rules processing and methodology forthe Optimizer Engine.

FIG. 7 is a flowchart of the steps taken during an interaction with acustomer according to one embodiment of the invention.

FIG. 8 shows two example of an end-to-end solution using the ExperienceOptimizer Engine.

FIG. 9 illustrates how one preferred embodiment of the invention createscode appropriate to a certain channel.

FIG. 10 illustrates how the rules engines leverages overriding rules(such as override rules, trigger rules and event-based rules) andworkbench-created interaction rules to choose treatments for a customerexperience.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In broad terms, the present invention is a method, system and computerprogram that a company may use to maximize the value of its variousinteractions with its customers. Certain aspects of the inventioninclude: (1) the methodology itself, (2) a software workbench thatguides a user through the methodology and assists the user with settingup interaction rules, and (3) a computer system that uses a centralized,channel independent, interaction engine with the interaction rules tocustomize/enhance the interactions with customers. In some embodiments,the interaction with the customers is improved after insight is derivedfrom past interactions.

FIG. 1 is a flowchart of the major steps involved in one preferredembodiment of the methodology. Using this holistic methodology, acompany may intelligently apply CRM strategies to its interactions withcustomers by enhancing or customizing those interactions. While acompany may not perform each of the suggested steps, or may perform someof them in parallel rather than sequentially, in the preferredembodiment, a company begins by evaluating its customer strategy 110.Then a customer segmentation may be performed on an organizationscustomer base 120. Based on certain value opportunities, an interactionstrategy may be formulated 130. Correlated to this strategy, a series ofexperiences and treatments may be defined, prioritized, and automated140. Each customer experience and treatment may then be delivered andexecuted 150 through an Optimizer Engine. This may enhance thecustomer's experience based upon the original defined experiences. Bymonitoring and gathering the results of the customer interactions 160,the company may derive additional insight in order to improve any of thepreviously mentioned stages of the process.

FIGS. 2-1, 2-2 and 2-3 illustrate the technology that may support themethodology outlined in FIG. 1. FIG. 2-1 shows the system as a series ofinterrelated components. As shown, the system may be dividedconveniently into a workbench analysis subsystem 200 (i.e., the softwareworkbench) and an interaction optimizing subsystem 202 (i.e., the systemwith the interaction engine). In one embodiment, the workbench analysissubsystem 200 is leveraged to evaluate the strategy 110, to identify thesegments 120, to form the interaction strategy 130, to define theexperiences 104 and perhaps to monitor the results 160. This subsystemconsists preferably of a personal computer (or other computing platform)204 running a software workbench application 205, which is incommunication with one or more databases 210 including the CustomerExperience Repository 210.1 which is where the Workbench stores itsdata.

The interaction optimizing subsystem 202 is preferably used to apply andexecute the experiences 150 during interactions with customers. Itconsists preferably of a technical architecture of one or more databases210 in communication with a web services layer 225 and a set of commoncustomer services 230. The web services layer 225 may be based onMicrosoft's .NET architecture or other architecture platforms. From theservices layer 225/230, a Customer Interaction Record 240 may be used totransfer data to and from an experience optimizer engine 245, which ispreferably built around a rules-based engine. The experience optimizerengine 245 may use the customer treatment data stored in the CustomerExperience Repository 210.1 by the workbench analysis subsystem 200 inorder to customize/optimize the interactions with customers.

FIG. 2-2 is a block diagram illustrating a preferred technicalarchitecture for the system from FIG. 2-1. The Channel Technologycomponent 215 may be any interaction channel that a customer mayinterface with an organization. This includes self service and non-selfservice capabilities. Examples include, but are not limited to, AgentDesktop (i.e. Siebel CRM System, SAP CRM System), IVR/SpeechApplications (i.e. Avaya Conversant, Nortel Periphonics, Nuance,Speechworks), Web Servers/Applications (i.e. Microsoft IIS), E-MailManagement (i.e. Kana ERMS, Siebel Mail, Microsoft Exchange) and otherchannels including Point of Sale, PDA, and Kiosk. The services layer225, 230 may be the underlying architecture to seamlessly interfacemultiple channels using the same protocols and common services. In oneembodiment, the Services Layer is built leveraging Microsoft's Webservices and Net technology. The Customer Interaction Record (CIR) 240may be a string or record of customer information generated to create a“Profile” of the customer for the purposes of providing up-to-date,insightful, and relevant information between the Services Layer 225/230and the Engine 245. In one embodiment, the Engine Technology 245 isbuilt on Microsoft's Biztalk 2004 rules engine and leverages alreadypre-defined policies, rules, vocabularies, and Net classes. The CustomerExperience Repository 210.1 is preferably a database that maintains allof the treatment data that may define a customer experience. Ultimately,any data stored in the Workbench 205 preferably will be captured in thisCustomer Experience Repository. The Technology for the Workbench is .NetASP web pages hosted on a Microsoft web server and runs a customizedworkbench application that captures segments, interactions, treatments,and experiences.

FIG. 2-3 is a diagram of a preferable example CIR 240 structure TheCustomer Interaction Record may aim at collating real-time 246 and batchattributes 242 of the customer to provide a summarized view of thecustomer. This summarized view will be used by the Experience OptimizerEngine (EOE) to determine the ultimate experience for a customer and tocommunicate the necessary customer interaction information to theappropriate customer interaction channel 215 through the Services Layer225/230. This CIR is an XML structure and would be generated through aweb service request and would interact with the Optimizer Engine throughweb services. The CIR may include a combination of Customer DefinitionData (e.g. name, address); Business Transaction History (e.g. CustomerPurchase History); Customer/Channel Preference Data (e.g. stated andimplied preferences); and Channel Interaction Data (e.g. interactionchannel choice and frequency), etc. The Customer Interaction Record(“CIR”) may be broken down into three sections: a batch data section, acustomer experience packet (CEP) section, and a real time data section.Fields in the CIR may be retrieved from a customer experience repository210.1 or other customer centric databases 210.2. Fields in the batchdata section of the CIR may include a customer field, a contact field,an address field, a household identification field, a segmentidentification field, account information, overriding data, triggerdata, and the like. The Customer Interaction Record may include acustomer experience packet (“CEP”) 244 for each treatment to bepresented to the customer during the interaction. As will be describedbelow, the treatment data may be populated by the optimizer engine 245to customize the experience for the individual customer. The real timedata 246 portion of the CIR 240 may consist of fields that relate not tothe customer's historical information, but to real time conditions, suchas the current interaction reason, web click stream history, or theidentification of a purchase during the existing transaction. As isillustrated in FIG. 2-1, the CIR 240 may be used to pass informationbetween the optimizer engine 245 and the services layer 225/230. The CIR240 may also contain data resulting from certain customer scores such asa credit risk score. In one example, leveraging this type of scoringdata may allow the engine to determine if a customer is eligible for aninterest free financing offer. There are many different types ofcustomer scoring that can be leveraged to determine the appropriatetreatments.

Phase 1: Evaluating a Customer Strategy (Step 110)

Now that the components of the experience optimizer engine 245 have beenpresented, the reader may better appreciate how treatments can bepersonalized for each customer with the intention that the customer'sexperience will be enhanced. Such enhancement is not made haphazardly.Rather, as FIG. 1 shows, the application of the rules to the treatmentdata 150 is a function of the preliminary analysis 110-140. FIGS. 3-1through 3-23 show several screen prints from the workbench application205 that is preferably used to perform such analysis. These figures showonly one representation of the various aspects of the present invention.The aspects may be incorporated as part of other software systems usingvarious techniques well known to those skilled in the art. While thescreen prints in FIG. 3 show the present invention as an end-to-endsolution, one skilled in the art will recognize that the featuresoffered by the present invention may be implemented as one or morecomponents in a company's present enterprise system.

In one embodiment, certain aspects of the invention are built within asoftware application known as the Customer Experience Workbench. Thisapplication has been discussed in FIGS. 2-1 and 2-2 as the workbenchanalysis system 200. Generally, the workbench is a software product thatmay guide a user through the analysis steps of the invention'ssystematic methodology and may assist the user with defining treatmentsfor customer interactions. FIG. 3-1 shows the entrance page for theworkbench after the user logs in. (The user may be a marketing manager,business analyst or other non-IT employee of the company. However, thecompany's IT personnel as well as independent contractors or consultantsmay also use the Workbench to set up and to maintain the system.) Alongthe left side of the screen, the various phases of the methodology(discussed above and shown in FIG. 1) are shown as folders. As thedifferences between the folders in FIG. 3-1 and the flowchart of FIG. 1show, the present invention may be described using differingterminology. Each of the folders from FIG. 3-1 may contain one or morechecklists or other work aids that may be accessed via the workbenchtool. FIG. 3-2 shows the workbench's checklist of tasks that areavailable to the user. As the user progresses through these tasks, theycan be displayed as completed through the use of checkmarks in theappropriate boxes.

The first step of assisting a company to implement insight-driveninteraction may be to evaluate a customer strategy for the company(110). This step gives an organization the chance to review and enhanceits customer strategy for marketing, sales, service, etc. to answerquestions such as “What markets or distribution channels support futuregrowth?” and “How are products and services driving value fromcustomers?” During this step, organizations may identify projections andassumptions around cost-to-serve and revenue opportunities so that theycan envision and focus on the value levers that help drive key customercentric costs and revenue strategies. In one embodiment, this strategymay be subdivided into three tasks, as shown in FIG. 3-2. First, thebusiness value drivers may be validated 3210. Identifying both financialand non-financial value drivers may help identify, drive, and prioritizeareas where the organization can impact its bottom line. To accomplishthis, a project team may review short and long term growth projectionsin the areas of marketing, sales and service (i.e. customer retentionprojections, cross-sell/up-sell rates, self-service projections, etc.)They may assess all potential value drivers and the manageability ofthese drivers including timeframes to execute, potential costs toimplement and impact on an organization and customer satisfaction. Theymay also prioritize value drivers based on results from assessments orsensitivity analysis.

The second step may be to define and gather data for key performanceindicators (“KPIs”) 3220. Identified KPIs can be utilized to betteralign organizations goals with a customer centric strategy. It isimportant to understand how KPIs are directly affected by costcomponents, potential treatments, or other customer focused initiatives.Some broader KPIs may be metrics such as “cost to serve” or “cost tomarket” while more detailed KPIs may be “cost per campaign” or “revenueper subscriber”. Understanding the impact that a customer strategy mayhave on a company's KPIs may be a continual process that is revisitedthroughout a customer blueprinting process.

The third step may be to define various types of operational constraints3230. Operational constraints may pertain to operations, such as whethera product is or is not available in a market area. Operationalconstraints may also be directed to policy constraints, such asindicating that accounts in collections should be redirected to thecollections department. Operational constraints may also be strategicimperatives, such as to gain market share or to gain share in a specificZIP code region. All such constraints may impact the way in whichcertain customers may be treated.

Phase 2. Identifying Customer Segments (Step 120)

Once the strategy has been evaluated 110, the next phase may be toidentify the customer segments 120. Customer segmentation arrangescustomers, or a representative sample of customers, into groupings ofcustomers (of perhaps six to nine groups, for example) where thecustomers in a given segment share one or more similar characteristics.These segmented groups may be used to drive an interaction strategyand/or design.

The consulting team or company representatives may choose to usesegmentation data that already exists or that the company may createitself. It may also be decided to have the consulting team or anotherthird party segment the company's customer population based on a numberof demographic and behavioral characteristics. While there are variousways to generate the customer segments, the utility patent applicationtitled “Multi-Dimensional Segmentation for Use in a CustomerInteraction”, (Ser. No. 10/302,418, filed on Nov. 22, 2002, which isincorporated herein by reference) teaches how to achieve better resultsby segmenting the customers across more than a single dimension and isincorporated herein by reference. In connection with themulti-dimensional segmentation taught in that application, the utilityapplication titled “Standardized Customer Application And Record ForInputting Customer Data Into Analytic Models” (Ser. No. 10/302,337,filed on Nov. 22, 2002, which is incorporated herein by reference)teaches one approach to using standardized flat records as a step in thesegmentation and/or customer interaction procedures. FIG. 3-3 shows thearea in the workbench that may be used to capture the segments by name3310 along with a short description 3320.

There may also be the ability in the workbench to drill down from thescreen shown in FIG. 3-3 to report on specific details about thesegments. FIG. 3-4 shows how the workbench may offer a means to reportmore detail about the segments that have been defined. FIG. 3-5 showshow a user can obtain reporting details around segments, notcharacteristic specific 3510. As FIG. 3-6 shows, a user may drill downwithin the segments to receive further detailed information about thespecific customers who fall within that segment 3610.

Beyond identifying segments, there may be needs to create sub-segmentsas illustrated in FIG. 3-7. The list of questions provided by theworkbench concerning the various types of operational constraints may beone method in deciding whether sub-segments may need to be developed.Sub-segments may be formed, for example, when the company wishes totarget a specific group of customers within a segment, such as inresponse to a competitor's offer in a specific region in the country. Inthis example, an organization may create a sub-segment called “<Segmentname>East” that presents specific offers to only people in a segmentthat live on the east coast.

Phase 3. Forming an Interaction Strategy (Step 130)

The segmentation phase 120 may be followed by the phase to form aninteraction strategy 130, which may be handled by performing a businessvalue assessment. In one embodiment of the workbench, there are fivetasks in this phase (that are listed as tasks in the screen of FIG.3-8), namely: define interaction reasons 3810, capture current channelvolumes 3820, capture current experiences 3830, optimize (i.e., enhance)segment strategy 3840, and model value opportunities 3850. This phasegives the organization the chance to strategically evaluate the reasonsas well as the ways they are currently interacting with their customers.It also allows them to identify a baseline for the way they areinteracting with customers today. “Define interaction reasons” 3810provides a way to capture each and every method a customer may interactwith the organization. The workbench 205 tool may have a predefined,industry specific, list of interaction reasons that can immediately beleveraged by a team. FIG. 3-8 illustrates a list of predefinedinteraction reasons for telecommunications 3860 as well as the abilityto read descriptions of the interaction reasons or delete them asappropriate 3870. A user may also want to add new interaction reasonsbased on their business 3880.

The step referred to as “Capture current channel volumes” 3820 allows anorganization to capture the number of times a segment of customers maycontact an organization for each interaction reason through eachchannel. This volume count is illustrated in FIG. 3-9 and percentages ofthese counts are illustrated in FIG. 3-10. “Capture current experiences”3830 allows an organization the ability to define the current functions,capabilities, and potentially content that segments of customers areexperiencing for each interaction reason, on each channel. In FIG. 3-11,there is shown the ability to document the current experiences 31110 foreach segment and interaction reason as well as document the capabilities31120.

“Model Value Opportunity” 3850 is a value calculator that allows anorganization the capability to identify the cost and revenue valuelevers that drive the business to deliver specific treatments tosegments of customers. FIG. 3-12 illustrates the area in the workbench205 tool in which data may be inputted, such as cost/revenue metrics31210 and value lever assumption 31220 (i.e. increase self service by20%). Examples of cost metrics include the current hourly wage of anagent, the cost per IVR interaction, or the total number of interactionthat occur within a year. Examples of revenue metrics may be the averagerevenue per customer or the margin on revenue. Examples of value leversmay include, % expected to increase cross-sell rate or % expected toincrease calls that will be completely answered within the firstinteraction (i.e. first call resolution). There is also the ability toview reports once data has been inputted 31230. FIG. 3-13 depicts someof the more detailed interaction metrics and a method to input the data31310. FIG. 3-14 illustrates a reporting screen called Summary TotalBenefits 31410 that is calculated based on all the inputted cost andrevenue data as well as value levers. The output of this report showsthe potential benefits that could occur if specific value levers, suchas self service, are increased or decreased from X % to Y %.

FIG. 3-15 shows how the workbench 205 may assist in the ranking ofinteraction reasons as well as the selection of which interactionreasons an organization will focus on automating treatments for. Onestep in managing interaction reasons may be the process of determiningwhich interaction reasons have the most impact on an organization (i.e.30% of all calls are billing inquiry), rank 31520 these interaction byimportance to the organization and finally select which interactionreasons to use 31530 or assign treatments to. In the screen shown inFIG. 3-15, a user has ranked her top four interactions to focus on. Forexample, out of all of the possible interactions that occur between acustomer and the company, the business user has identified “Bill-GeneralInquiry” 31520 to be the first interaction to focus on. She has alsochecked the use box 31530 to illustrate that she wants to associatetreatments to apply to this interaction reason. By allowing the user toprioritize any number of interactions, the company may start affectingspecific interactions slowly and then build up to apply treatments tomore interactions over time.

FIG. 3-16 depicts an area in the workbench 205 where a future channelmix may be defined 31610. In the embodiment shown in the figure, onlythe previously selected interaction types are included in defining thefuture channel mix. This definition process allows an organization theability to set goals of which channels it wants certain types ofinteraction to communicate through. By completing this future channelmix process, an organization can view reports on where it is todayversus what its goals may be in driving certain types of interaction tospecific types of channels.

Phase 4. Defining and Automating Treatments (Step 140)

Once the segments, channels and interaction reasons have beenprioritized by the user, the next phase of work is preferably to definethese experiences or specific treatments so that they can be applied andautomated through the engine-245. As shown in FIG. 3-17, the workbench205 may allows business users to easily add 31720 and define varioustreatments. It also allows user to add or modify values and codesassociated with each identified treatment, as illustrated in FIG. 3-18.For example, a treatment may have the code of S1 which means “selfservice”. The IVR may then use the code S1 to illustrate that a personassociated with S1 should receive an IVR script that pushes forcustomers to remain in the IVR and complete self-service transactions. Acode of A1 or “Agent” may mean for the IVR to route the call directly toan agent. Business or technology users can assign or modify treatmentsin real-time as changes to the process are desired. For example, if acertain product offer is not gaining acceptance from a segment of users,that same product offer can quickly be offered to another segment ofcustomers. However, treatment codes and results must then be understoodby the channels for these changes to take place. This phase of managingtreatments also allows users of the workbench 205 to rank and selectwhich treatments should be leveraged by the engine 245. This screen inthe Workbench and process is illustrated in FIG. 3-19. While multipletreatments may be defined and ranked, it is not until the useridentifies specific treatments to be ‘used’ that the engine will thenexecute against it. This means that the treatments and values are storedin the Customer Experience Repository 210 and the channel technology 215understands the values as defined in the workbench 205.

The final preparation in the Customer Experience Workbench 205 may be toassign the future experiences and treatments 32010 (of FIG. 3-20). Thefirst step (defining future experiences 32020) allows the project teamor company representative to define the future experiences for eachsegment of customers 32040, for each interaction reason 32050, andwithin each channel. During this step a business user may leverage theworkbench 205 to capture the requirements regarding the future type ofexperience 32060 the selected segment should have on each channel andthe capabilities or functions 32070 that should apply to that segment32040 and that interaction reason 32050. There is also the ability totie experiences to specific types of content 32080 such as .wav filesfor the IVR or .jpg files for the web.

Now that all the segments, interactions, channels, and treatments aredefined, they may be tied together to automate the treatment. This mayhappen in the define treatment automation page of the workbench 205, asis illustrated in FIG. 3-21. FIGS. 3-21 allows a user to define anexperience by selecting a segment 32110, interaction type 32120, channel32130, and treatment 32140. Finally, by selecting the ‘details’ link32150, the user may be enabled to determine the exact treatment for thecustomer segment, as illustrated by FIG. 3-22. For example, during thedefine treatment step earlier in this workbench process (as describedabove) two codes were associated with a treatment called ‘IVR AgentAvailability’, namely S1 for self service and A1 for agent assisted.FIG. 3-22 may be the location within the software application where auser may specifically select which treatment a segment should receive(i.e. sent directly to an Agent or given a self service menu in theIVR). As illustrated in the example shown in FIGS. 3-21 and 3-22, theworkbench has defined a customer segment named “Loyal Core” (see 31210).When the “Choose Interaction Reason” field 31220 is used to select the“Bill: General Inquiry” reason, the treatment element “IVR AgentAvailability Treatment” may be chosen for the IVR channel 31260. Usingthe “Detail” button 31270, the user may determine that a customer in the“Loyal Core” segment, about a “Bill: General Inquiry”, will leverage the“IVR Agent Availability” treatment which is being configured (see 32240in FIG. 3-22) to send the customer directly to an agent.

As all the segments, interactions, channels, and treatments are definedand tied together, the data is captured and stored in the customerexperience repository 210.1 to be used to support future customerinteractions. By leveraging this repository 210.1, the present inventionmay allow a company to successfully define holistic and granularstrategies. Without assistance from comprehensive information stored onthe experience repository, a company may flounder in trying to makesense of and organize the myriad customer interactions and responsesthat take place across its various contact channels. Thus, theexperience repository 210.1 may make creating a blueprint of thecompany, its interactions, and its marketing strategies easier orquicker, or the repository may ensure that the resulting blueprint ismore robust.

Now that the treatments and experiences have been defined, there is agap analysis 32210 step that may be undertaken. The gap analysis may beused to define the differences between the existing experiences in eachchannel and the future experiences. This information may assist in theprioritization of any new implementations of experiences and treatments.

Next, a user may begin planning the implementation of all theexperiences and treatments with all the external applications includingthe Experience Optimizer and each of the channels identified during theblueprinting process. The first task in this step may be to beginreconciling the future experience plans as defined in the blueprint withthe implementation teams. This reconciliation process may assist indetermining the complexities, implementation timelines, and priorities.The next task may be to prioritize the experience capabilities based oncosts, benefits, complexity, etc. The last task in this step may be tocreate a benefits roadmap which is a timeline that illustrates whencapabilities will be launched and overall benefits expected based onimplementations of capabilities.

The last step in the workbench 205 before the ongoing monitoring of theexperiences may be to build rules in the Experience Optimizer Engine245. While this step is illustrated as part of the workbench process,the engine rule building happens outside of the workbench and within theExperience Optimizer Engine 245 itself. Defining and building theserules in the engine are described in further detail during Phase 5,below.

Phase 5. Delivering Rules and Executing the Optimizer Engine to Applythe Defined Experiences during Interactions with Customers (Step 150)

The previous phases shown in FIG. 1 (steps 110 through 140) may behandled by the consultant or business user leveraging the workbench tool205. In contrast, the act of defining and executing the experiencesduring interactions with customers 150 may be performed by aprocess/technology project team and executed by the InteractionOptimizing Subsystem 202 and its Experience Optimizer Engine 245.

The Experience Optimizer Engine (EOE) 245 is a software component thatmay ensure that the blueprinting exercise that occurred within theworkbench 205 is applied consistently across all channels to achieve thecustomer profitability objectives, as defined by the organization. Foreach interaction, it may resolve the customer segment, currenttransaction, and current channel in order to determine the appropriatepredefined treatment protocol to be executed. It can be invoked todeliver treatments to the channels, including messages, routinginstructions, service options, and/or product offers. The EOE 245 mayexecute rules that have been created for all customers. While theWorkbench defined specific treatments based upon customer segment,interaction type and interaction channel, the EOE may identify customerattributes or events that require pre-emptive treatments based on thisdata. For example, John Doe may be a customer that falls in a financialsegment that has been defined by the Workbench to get a “free wirelessphone” offer when logging onto the company's website. However, if theExperience Optimizer recognizes that John Doe just purchased a newphone, the Engine may then pre-empt the free phone offer and insteadpresent John Doe with a “$10 off his next Wireless Accessory Purchase”offer.

While the EOE may enable and operationalize treatments and experiences,it also may create an architecture solution that is flexible and robust.Organizations that are striving to deliver insight driven sales,service, and marketing consistently across contact channels are oftenchallenged by disparate systems which use different database structures,ID formats, and programming languages. Traditional approaches to insightdriven interactions result in the redundant implementation of criticalbusiness functions and rules, such as customer identification,evaluation, and treatment selection. The Interaction Optimizingsubsystem 202 may deliver a singular and consistent implementation ofsuch key functions via the Experience Optimizer Engine 245 and theCustomer Experience Repository 210.1. FIG. 5 illustrates such an abilityto consolidate logic. In the traditional approach 510, rules are storedand maintained for each channel 520. In the Experience Optimizerapproach offered by the present invention 530, the rules can becentrally stored 540 and maintained.

The Experience Optimizer Engine 245, as defined within the systemillustrated in FIG. 2-1, provides a preferred hierarchy to process therules within the Engine itself. This rules hierarchy is illustrated inFIG. 6 and may start by identifying rules that are termed as ‘overridingrules’ 610. “Overriding rules” 610 are often governed by various federallaws, company policies or by credit/risk related attributes ofcustomers. These rules should take precedence over all other rules. Thenext executed set of rules are often “trigger rules” 620. “Triggerrules” 620 are based on changes during the lifecycle of the customer.These triggers are not behavioral events but generally occur over aperiod of time. These changes provide good opportunities for an up-sellor cross-sell of products or services. Following “Trigger Rules” may be“Event Based Rules” 630. “Event Based Rules” 630 are reactionary rulesbased on very recent events that took place or an event that took placeduring the current interaction, such as just purchasing a product. Thefinal set of rules may be “interaction rules” 640. These “Interactionrules” 640 are the defined treatments that have been setup within theworkbench 205 and are ready to be executed from the Customer ExperienceRepository 210. A rule of thumb may be that the majority of rulesexecuted in this processing methodology should occur within the defined“interaction rules.” (Of course, in other embodiments, rules may becategorized differently.)

FIG. 7 is a flowchart of the steps taken during an interaction with acustomer according to one embodiment of the invention (as shown in FIG.2-1). A customer may contact the company and makes a request of thecompany through one of several available communication channels. Thechannels may include the web (accessed via a personal computer or PDA)215.1, the IVR accessed with a phone 215.2, email, wireless phone, thecontact center (accessed by calling a contact agent), or other channelsnow known or later developed 215.N. The interface to the communicationchannel (such as the IVR) receives the request 705/220 and builds acustomer interaction record (“CIR”) 710 based on who the customer isidentified to be, the reason for the contact, and information about thecustomer. From this record, an XML document is preferably built 715 tobe used to transfer the CIR contents to the web services layer 225 andbeyond to the experience optimizer engine 720/245. In one preferredembodiment, the engine 245 may execute a common customer service callGetTreatment. This web service would then trigger a policy in the rulesengine and begin the rules processing.

The engine 245 may then begin the rules processing as described in FIG.6 by first assessing the overriding rules 725/610, then trigger rules730/620, then event based rules 730/630 and finally the interactionrules 730/640. When all the final treatments are identified per arequest 405, the customer experience packet (“CEP”) 244 shown in FIG.2-3 may be updated with values to indicate the customization to one ormore defined treatments. For example, if the caller is to be routed tocollections based on an EOE rule, then the treatment A section of theCEP may be updated with a special code that will instruct the IVR totransfer the call rather than present the customer with further options.

Once the rules engine 245 has processed all applicable rules, it mayfinalize the treatments 735 by creating an action that may pass thefinal defined treatments with values back to the channel interface. Inone embodiment this is accomplished by translating the CIR data 240 onceagain into an XML document and passing that document as a response 250through the web services layer 225 to the channel interface 740. Thevarious channel interfaces must be already modified so that they caneach accept such an XML document and modify the treatments to present tothe customer based on values in the document 745.

An end-to-end example of how the insight driven interaction process isaccomplished, using the Experience Optimizer Engine 245, can be seen inFIG. 8. The top of this figure depicts an example request of a customerin the IVR requesting a billing inquiry 805. Based on these 3 pieces ofdata (customer ID, channel ID and transaction ID), the IVR makes a webservice request called GetTreatment 810. In this one embodiment of thesolution, the GetTreatment triggers the creation of the CIR for thisspecific customer 815. Once formatted into a XML document 820, the CIR240 is delivered to the EOE 245. When the EOE 245 receives theGetTreatment request, including the CIR 240 data, it reads it andtriggers the first policy (i.e. set of rules) 825 in the EOE. Thispolicy begins the rules processing methodology described in FIG. 6. Inthe example depicted in FIG. 8, overriding rules 830/610 are firstanalyzed. If the CIR provides the engine with data that triggers anoverriding rule to true, then this example creates a treatment called“route call to collections” 835, which then updates the CIR string 840,and sends a web service response 845 back to the channel informing theIVR to route the call to collections as depicted in “option 1” 855 inthe diagram. However, if the overriding rule is false 890, the enginewill first get all the treatment data as defined by the CEW. It willthen identify if any trigger rules 865/620 are true and update theoriginal treatment data with trigger rule data 870. This same processwill happen again with event based data 875 until the CIR string isfully updated with all final treatments 880. In this example, treatment#6 and #7 have been updated based on trigger and event based rules. Afinal web service response 845 will be delivered and an updated CIR XMLdocument will be created 850. The last step will be delivering thisdocument with seven treatments to the IVR channel. These finaltreatments are depicted as “option 2” 885 in the diagram of FIG. 8.

While the interaction processing of FIGS. 7 and 8 is useful for arequest from any type of communication channel, one embodiment of theinvention has been developed specifically to handle self-serviceinteractions. Such interactions are those in which the customer seeks tohelp himself or herself without human intervention. The channelscommonly used for self-service interactions are most frequently the IVR,web, and email. Other communication channels now known or laterdeveloped may also be used for self-service interactions.

FIG. 9 illustrates how one preferred embodiment of the invention createscode appropriate to a certain channel. When the request from any of thecommunication channels 215 is processed by the optimizing subsystem 202,the response may be in XML 910 or other channel-appropriate format, suchas VXML 905, XHTML 915, SALT 920 or other format now known or laterdeveloped.

FIG. 10 is another conceptual illustration showing how the rules enginesmay leverage overriding rules (such as override rules, trigger rules andevent-based rules) as well as workbench-created interaction rules tochoose treatments for a customer experience. In FIG. 10, one of thechannels 215 may send a request to the system's services layers 225/230.The services may send the request (via a CIR) to the engine 245. Theengine may begin by applying various overriding rules, such as overriderules (rule 1), trigger rules (rule 2), and event-based rules (rule 3).Then the engine 245 may process the interaction rules that were set upthrough the workbench subsystem. To do this, the engine 245 may accessthe blueprint stored in the repository 210 to determine whichtreatment(s) are appropriate based on the customer's segment, channel,interaction type, etc. For example, the interaction rules derived fromthe blueprint shown in FIG. 10 indicate that if a customer using the IVRis in the SILVER segment and if that customer is making a billinginquiry request, then treatment number 830 should be applied. Once theengine 245 has complied all of the treatments appropriate to therequest, they are returned to the channel 215 for presentation to thecustomer.

Phase 6. Monitoring Results of Customer Interactions (Step 160)

The workbench may offer the user an ‘Experience Monitor’ asset tomonitor the status of experiences and provide data that will lead toenhanced results. The Experience Monitor is a diagnostic tool that usesbusiness performance metrics and customer level data across customerexperiences to diagnose performance issues and highlight opportunities.The value of this tool comes from eliminating the need to review all thehundreds of experiences defined within the Customer Experience Workbench205. The analysis is accomplished using statistical analysis to focus inon the experiences whose value is misaligned with business expectations.Users can then focus their efforts on revising those experiences toincrease the overall value.

The Experience Monitor solution may provide the data and processes tomeasure key performance metrics for each defined experience as well asmethods to assess drivers for overall value and for each performancemetric. It may measure the value of an experience with a single metricthat encompasses all the key revenue and cost drivers including AverageHandle Time, Cost per Contact, Sales Opportunities & Conversion,Satisfaction, and Retention. This solution provides methods to identifyexperiences performing well and poorly along with suggestions on how tomodify the experience for better outcomes.

The ‘experience monitor’ may reside within the workbench 205 and maycreate as well as display reports related to the customer experience.One embodiment of the experience monitor is illustrated in FIG. 3-23.FIG. 3-23 also illustrates a dashboard to the experience monitor assetwhere a user can control the viewing of specific reports based on theirneeds. By monitoring the results, a closed loop of processing may bemade. This closed loop may allow the company to derive insight from theresults and apply that insight to future experiences with the same orother customers.

In one embodiment of the invention, insight about the customer isderived from customer data, results of past interactions, and the like.In such an embodiment, the insight is used to create specific rules andtreatments to be offered to the customer through the engine 245. Detailsand the benefits of deriving and leveraging customer insight throughclosed-loop processes such as this has been described in thecommonly-owned utility patent application titled “Adaptive MarketingUsing Insight Driven Customer Interaction” (Ser. No. 10/302,395, filedon Nov. 22, 2002) which is incorporated herein by reference.

While the specification describes particular embodiments of the presentinvention, those of ordinary skill can devise variations of the presentinvention without departing from the inventive concept. For example,while examples discussed above have sometimes centered around the IVRand/or web channels, the present invention may be implemented for anycommunication channel now known or later developed. As another example,the workbench tool 205 may be programmed with user interface screensthat differ from those shown in FIGS. 3-1 through 3-24. In the drawingsillustrating the present invention, elements on the various drawings mayrepresent the same or similar components, whether or not they arenumbered the same.

1. A method for optimizing customer experiences, the method comprising:defining a plurality of prioritized experiences correlating to acustomer interaction strategy, wherein each prioritized experience hasat least one associated treatment; storing the plurality of prioritizedexperiences for consistent treatment among a plurality of channels;dynamically applying the plurality of stored defined experiences duringinteractions with customers; and capturing customer interaction results,for refining future targeted interactions.
 2. The method from claim 1,further comprising: evaluating a customer strategy for a company;identifying a plurality of customer segments for a customer base of acompany; and formulating an interaction strategy based on valueopportunities.
 3. The method from claim 1, further comprising derivinginsight about customers from analytical models, wherein defining theprioritized experiences is based on the derived insight.
 4. The methodfrom claim 1, wherein the step of storing the plurality of prioritizedexperiences stores experience data in a central repository; and whereinthe step of dynamically applying the plurality of defined experiencesretrieves experience data from the central repository.
 5. The methodfrom claim 3, wherein the step of deriving insight from analyticalmodels comprises: extracting customer data for a plurality of customersfrom at least one database; training analytical models to predictcustomer behavior, wherein the analytical models are trained using thecustomer data extracted from at least one database; gathering thecustomer interaction results; and re-training the analytic models torefine the customer behavior prediction, wherein the analytical modelsare re-trained using the customer data extracted from at least onedatabase as well as the customer interaction results.
 6. The method fromclaim 2, wherein evaluating the customer strategy comprises: evaluatingbusiness value drivers; defining key performance indicators; anddefining business constraints.
 7. The method from claim 2, whereinidentifying the plurality of customer segments comprises: segmenting aplurality of customers by behavior data stored in a data warehouse;segmenting the plurality of customers by value data stored in the datawarehouse; and generating a two-dimensional matrix for cross-segmentingthe plurality of customers by both behavior data and value data.
 8. Themethod from claim 2, wherein formulating the interaction strategycomprises choosing a subset of interaction reasons from a pre-definedrepository of interactions for a specified industry.
 9. The method fromclaim 2, wherein the step of formulating the new interaction strategycomprises capturing current channel mix for all experiences and futurechannel mix for prioritized experiences.
 10. The method from claim 2,wherein the step of formulating the interaction strategy comprisesmodeling value opportunities.
 11. The method from claim 2, whereinformulating the interaction strategy comprises ranking interactionreasons to determine a primary set of interaction reasons.
 12. Themethod from claim 2, wherein formulating the interaction strategycomprises: defining a plurality of treatments; and assigning each of theplurality of treatments to a prioritized interaction.
 13. The methodfrom claim 12, wherein the step of assigning is based on a hierarchy ofgrouped rules.
 14. The method from claim 1, wherein the step of definingthe plurality of prioritized experiences enables a business user todefine a plurality of treatments.
 15. The method from claim 1, whereinthe step of dynamically applying the plurality of defined experiencescomprises leveraging a centralized rules engine; wherein the rulesengine is independent of and consistent for a plurality of channels. 16.The method from claim 15, wherein the rules engine applies treatments asa function of a customer segment, an interaction type, and aninteraction channel.
 17. The method from claim 1, wherein the step ofapplying the plurality of defined experiences comprises: building acustomer intelligence record for representing a plurality of data fieldsfor a customer; passing the customer intelligence record to a central,channel-independent rules engine; processing a plurality of rules forupdating the customer intelligence record to indicate at least onetreatment for the customer; and sending data from the customerintelligence record to the channel for instructing the channel on thetreatments to present to the customer.
 18. The method from claim 17,further comprising scoring customer information; and storing scoredinformation in the customer intelligence record.
 19. The method fromclaim 1, wherein the plurality of prioritized experiences supportmarketing, sales, service and billing functions executed by a customer.20. The method from claim 2, wherein formulating an interaction strategyincludes assessment of a business and identification of opportunities tocreate value.
 21. A computer program stored on a computer readablemedium for execution by a computer, the computer program comprising: acode segment for defining a plurality of prioritized experiencescorrelating to an interaction strategy, wherein each prioritizedexperience has at least one associated treatment; a code segment forstoring the plurality of prioritized experiences for consistenttreatment among a plurality of channels; a code segment for dynamicallyapplying the plurality of stored defined experiences during interactionswith customers; and a code segment for capturing customer interactionresults, for refining future targeted interactions.
 22. The computerprogram from claim 21, further comprising: a code segment for evaluatinga customer strategy for a company; a code segment for identifying aplurality of customer segments for a customer base of a company; and acode segment for formulating an interaction strategy based on valueopportunities.
 23. The computer program from claim 21, furthercomprising a code segment for deriving insight about customers fromanalytical models, wherein defining the prioritized experiences is basedon the derived insight.
 24. The computer program from claim 21, whereinthe code segment for storing the plurality of prioritized experiencesstores experience data in a central repository; and wherein the codesegment for dynamically applying the plurality of defined experiencesretrieves experience data from the central repository.
 25. The computerprogram from claim 23, wherein the code segment for deriving insightfrom analytical models comprises: a code segment for extracting customerdata for a plurality of customers from at least one database; a codesegment for training analytical models to predict customer behavior,wherein the analytical models are trained using the customer dataextracted from at least one database; a code segment for gathering thecustomer interaction results; and a code segment for re-training theanalytic models to refine the customer behavior prediction, wherein theanalytical models are re-trained using the customer data extracted fromat least one database as well as the customer interaction results. 26.The computer program from claim 22, wherein the code segment forevaluating the customer strategy comprises: a code segment forevaluating business value drivers; a code segment for defining keyperformance indicators; and a code segment for defining businessconstraints.
 27. The computer program from claim 22, wherein the codesegment for identifying the plurality of customer segments comprises: acode segment for segmenting a plurality of customers by behavior datastored in a data warehouse; a code segment for segmenting the pluralityof customers by value data stored in a data warehouse; and a codesegment for generating a two-dimensional matrix for cross-segmenting theplurality of customers by both behavior data and value data.
 28. Thecomputer program from claim 22, wherein the code segment for formulatingthe interaction strategy comprises a code segment for choosing a subsetof interaction reasons from a pre-defined repository of interactions fora specified industry.
 29. The computer program from claim 22, whereinthe code segment for formulating the new interaction strategy comprisesa code segment for capturing current channel mix for all experiences andfuture channel mix for prioritized experiences.
 30. The computer programfrom claim 22, wherein the code segment for formulating the interactionstrategy comprises a code segment for modeling value opportunities. 31.The computer program from claim 22, wherein the code segment forformulating the interaction strategy comprises a code segment forranking interaction reasons to determine a primary set of interactionreasons.
 32. The computer program from claim 22, wherein the codesegment for formulating the interaction strategy comprises: a codesegment for defining a plurality of treatments; and a code segment forassigning each of the plurality of treatments to a prioritizedinteraction.
 33. The computer program from claim 32, wherein the codesegment for assigning is based on a hierarchy of grouped rules.
 34. Thecomputer program from claim 21, wherein the code segment for definingthe plurality of prioritized experiences enables a business user todefine a plurality of treatments.
 35. The computer program from claim21, wherein the code segment for dynamically applying the plurality ofdefined experiences comprises a code segment for leveraging acentralized rules engine; wherein the rules engine is independent of andconsistent for a plurality of channels.
 36. The computer program fromclaim 35 wherein the centralized rules engine applies treatments as afunction of a customer segment, an interaction type, and an interactionchannel.
 37. The computer program from claim 21, wherein the codesegment for applying the plurality of defined experiences comprises: acode segment for building a customer intelligence record forrepresenting a plurality of data fields for a customer; a code segmentfor passing the customer intelligence record to a central,channel-independent rules engine; a code segment for processing aplurality of rules for updating the customer intelligence record toindicate at least one treatment for the customer; and a code segment forsending data from the customer intelligence record to the channel forinstructing the channel on the treatments to present to the customer.38. The computer program from claim 37, further comprising: a codesegment for scoring customer information; and a code segment for storingthe scored information in the customer intelligence record.
 39. Thecomputer program claim from 21, wherein the plurality of prioritizedexperiences support marketing, sales, service and billing functionsexecuted by a customer.
 40. The computer program claim from claim 22,wherein formulating an interaction strategy includes assessment of abusiness and identification of opportunities to create value.
 41. Asystem for optimizing customer experiences, the system comprising: aworkbench analysis subsystem for defining a plurality of prioritizedexperiences correlating to an interaction strategy, wherein eachprioritized experience has at least one associated treatment; a centralrepository for storing the plurality of prioritized experiences forconsistent treatment among a plurality of channels; an interactionoptimizing subsystem for dynamically applying the plurality of storeddefined experiences during interactions with customers; and a subsystemfor capturing customer interaction results, for refining future targetedinteractions.
 42. The system from claim 41, wherein the interactionoptimizing subsystem comprises: a rules-based engine for choosing fromthe plurality of prioritized experiences in the central repository; anda plurality of services for interfacing data between the rules-basedengine and the plurality of communication channels.
 43. The system fromclaim 42, wherein the plurality of services comprise: a plurality of webservices; and a plurality of common customer services.
 44. The systemfrom claim 41, further comprising a plurality of customer segments for acustomer base of a company; and an interaction strategy module forformulating an interaction strategy based on value opportunities. 45.The system from claim 41, further comprising at least one analyticalmodel for use in deriving insight about customers, wherein the derivedinsight is leveraged by the workbench analysis subsystem for definingthe prioritized experiences.
 46. The system from claim 44, furthercomprising: at least one database upon which is stored customer data;wherein the at least one analytical model is trained to predict customerbehavior using customer data extracted from the at least one database;and wherein the at least one analytical model is re-trained using thecustomer data extracted from the at least one database and the gatheredcustomer interaction results from the subsystem for capturing customerinteraction results.
 47. The system from claim 44, further comprising: afirst set of customer segments based on behavior data stored in a datawarehouse; a second set of customer segments based on value data storedin the data warehouse; and a two-dimensional matrix for cross-segmentingthe plurality of customers as a function of the first set of customersegments and the second set of customer segments; wherein the pluralityof customer segments are determined from the two-dimensional matrix. 48.The system from claim 41, further comprising a pre-defined repository ofinteractions for a specified industry; wherein the workbench analysissubsystem leverages the pre-defined repository of interactions fordefining the plurality of prioritized experiences.
 49. The system fromclaim 44, wherein the interaction strategy module captures currentchannel mix for all experiences and future channel mix for prioritizedexperiences.
 50. The system from claim 44, wherein the interactionstrategy module models value opportunities.
 51. The system from claim44, wherein the interaction strategy module ranks interaction reasons todetermine a primary set of interaction reasons.
 52. The system fromclaim 44, wherein the interaction strategy module defines a plurality oftreatments and assigns each of the plurality of treatments to aprioritized interaction.
 53. The system from claim 52, wherein theinteraction strategy module bases the assignment on a hierarchy ofgrouped rules.
 54. The system from claim 41 wherein the workbenchanalysis subsystem enables a business user to define a plurality oftreatments.
 55. The system from claim 42, wherein the rules-based engineis independent of and consistent for the plurality of channels.
 56. Thesystem from claim 55, wherein the rules-based engine applies treatmentsas a function of a customer segment, an interaction type, and aninteraction channel.
 57. The system from claim 42, further comprising: acustomer intelligence record for representing a plurality of data fieldsfor a customer; wherein the customer intelligence record is passed tothe rules-based engine; wherein the customer intelligence record isupdated to indicate at least one treatment for the customer; and whereinthe customer intelligence record is passed to the channel forinstructing the channel on the treatments to present to the customer.58. The system from claim 57, further comprising a scoring module forscoring customer information; wherein scored information from thescoring module is stored in the customer intelligence record.
 59. Thesystem from claim 41, wherein the plurality of prioritized experiencessupport marketing, sales, service and billing functions executed by acustomer.
 60. The system from claim 44, wherein formulating aninteraction strategy includes assessment of a business andidentification of opportunities to create value.